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summary(cars)
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## randomForest 4.7-1
## Type rfNews() to see new features/changes/bug fixes.
##
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## last_plot
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## filter
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## layout
rand_forest <- randomForest(data = employee_trainset,
Attrition ~ .,
importance = TRUE)
predict_forest <- predict(rand_forest, employee_testset)
confusionMatrix(predict_forest,
employee_testset$Attrition,
positive = "Yes")
## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 369 53
## Yes 6 13
##
## Accuracy : 0.8662
## 95% CI : (0.8308, 0.8966)
## No Information Rate : 0.8503
## P-Value [Acc > NIR] : 0.1938
##
## Kappa : 0.2561
##
## Mcnemar's Test P-Value : 2.115e-09
##
## Sensitivity : 0.19697
## Specificity : 0.98400
## Pos Pred Value : 0.68421
## Neg Pred Value : 0.87441
## Prevalence : 0.14966
## Detection Rate : 0.02948
## Detection Prevalence : 0.04308
## Balanced Accuracy : 0.59048
##
## 'Positive' Class : Yes
##
#Tables
연습 인라이 r 30
import pandas as pd
3+5
## 8
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